Highlights in Research

Latent curve models are widely popular for the analysis of longitudinal data. These models allow researchers to study change and development in behavior at the level of the individual. This article takes a deeper approach to understanding the individual differences that are captured by the model. By using differential calculus, psychologists can gain greater insight into a longitudinal process at the level of the individual that goes beyond the typical interpretation of the model.

This two-stage study aims to survey the current practices regarding gender measurement in the quantitative social sciences and to make appropriate recommendations for future researchers that align with current biological, physiological medical, and humanistic knowledge about the non-binary, continuous nature of gender difference. The first phase of the study will include a comprehensive literature review across key quantitative journals in psychology and sociology, specifically looking at what measures of gender are typically used. We expect to find that researchers are using categorical, most often binary, measures of gender. Beyond posing problems for face validity, dichotomizing a variable known to be continuous in nature can lead to higher than expected error rates, incorrect inferences, and a failure to detect trends. Our goal is to publish a paper summarizing the state of gender measurement in quantitative social science research and calling for more appropriate ways to measure and scale gender (the paper’s working title is “The Mis-Measurement of Gender: Thinking beyond the binary in quantitative social science research”). The findings from phase one will then inform phase two of this study, in which we propose to devise a new measure that captures the fluid nature of gender. Though no scale will be a perfect, as the very act of “scaling” gender forces it into a fixed set of traits, our goal is to improve upon current practices. Most researchers are likely still using binary (or likewise overly-rudimentary) measures of gender because alternative options are lacking; consequently, providing an alternative will enable scholars to conduct better research as well as highlight the importance of devising metrics that adequately represent the real-life phenomena they purport to measure.